| 1. |
- Berndt, Sonja I., et al.
(författare)
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Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture
- 2013
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Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1061-4036 .- 1546-1718. ; 45:5, s. 501-U69
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Tidskriftsartikel (refereegranskat)abstract
- Approaches exploiting trait distribution extremes may be used to identify loci associated with common traits, but it is unknown whether these loci are generalizable to the broader population. In a genome-wide search for loci associated with the upper versus the lower 5th percentiles of body mass index, height and waist-to-hip ratio, as well as clinical classes of obesity, including up to 263,407 individuals of European ancestry, we identified 4 new loci (IGFBP4, H6PD, RSRC1 and PPP2R2A) influencing height detected in the distribution tails and 7 new loci (HNF4G, RPTOR, GNAT2, MRPS33P4, ADCY9, HS6ST3 and ZZZ3) for clinical classes of obesity. Further, we find a large overlap in genetic structure and the distribution of variants between traits based on extremes and the general population and little etiological heterogeneity between obesity subgroups.
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| 2. |
- Do, Ron, et al.
(författare)
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Common variants associated with plasma triglycerides and risk for coronary artery disease
- 2013
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Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1061-4036 .- 1546-1718. ; 45:11, s. 1345-
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Tidskriftsartikel (refereegranskat)abstract
- Triglycerides are transported in plasma by specific triglyceride-rich lipoproteins; in epidemiological studies, increased triglyceride levels correlate with higher risk for coronary artery disease (CAD). However, it is unclear whether this association reflects causal processes. We used 185 common variants recently mapped for plasma lipids (P < 5 x 10(-8) for each) to examine the role of triglycerides in risk for CAD. First, we highlight loci associated with both low-density lipoprotein cholesterol (LDL-C) and triglyceride levels, and we show that the direction and magnitude of the associations with both traits are factors in determining CAD risk. Second, we consider loci with only a strong association with triglycerides and show that these loci are also associated with CAD. Finally, in a model accounting for effects on LDL-C and/or high-density lipoprotein cholesterol (HDL-C) levels, the strength of a polymorphism's effect on triglyceride levels is correlated with the magnitude of its effect on CAD risk. These results suggest that triglyceride-rich lipoproteins causally influence risk for CAD.
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| 3. |
- Kanoni, Stavroula, et al.
(författare)
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Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis.
- 2022
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Ingår i: Genome biology. - : Springer Science and Business Media LLC. - 1474-760X .- 1465-6906 .- 1474-7596. ; 23:1
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Tidskriftsartikel (refereegranskat)abstract
- Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery.To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N=1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3-5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism.Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.
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| 4. |
- Lu, Yingchang, et al.
(författare)
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New loci for body fat percentage reveal link between adiposity and cardiometabolic disease risk
- 2016
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 7
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Tidskriftsartikel (refereegranskat)abstract
- To increase our understanding of the genetic basis of adiposity and its links to cardiometabolic disease risk, we conducted a genome-wide association meta-analysis of body fat percentage (BF%) in up to 100,716 individuals. Twelve loci reached genome-wide significance (P<5 × 10(-8)), of which eight were previously associated with increased overall adiposity (BMI, BF%) and four (in or near COBLL1/GRB14, IGF2BP1, PLA2G6, CRTC1) were novel associations with BF%. Seven loci showed a larger effect on BF% than on BMI, suggestive of a primary association with adiposity, while five loci showed larger effects on BMI than on BF%, suggesting association with both fat and lean mass. In particular, the loci more strongly associated with BF% showed distinct cross-phenotype association signatures with a range of cardiometabolic traits revealing new insights in the link between adiposity and disease risk.
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| 5. |
- Willer, Cristen J., et al.
(författare)
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Discovery and refinement of loci associated with lipid levels
- 2013
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Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1061-4036 .- 1546-1718. ; 45:11, s. 1274-1283
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Tidskriftsartikel (refereegranskat)abstract
- Levels of low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, triglycerides and total cholesterol are heritable, modifiable risk factors for coronary artery disease. To identify new loci and refine known loci influencing these lipids, we examined 188,577 individuals using genome-wide and custom genotyping arrays. We identify and annotate 157 loci associated with lipid levels at P < 5 x 10(-8), including 62 loci not previously associated with lipid levels in humans. Using dense genotyping in individuals of European, East Asian, South Asian and African ancestry, we narrow association signals in 12 loci. We find that loci associated with blood lipid levels are often associated with cardiovascular and metabolic traits, including coronary artery disease, type 2 diabetes, blood pressure, waist-hip ratio and body mass index. Our results demonstrate the value of using genetic data from individuals of diverse ancestry and provide insights into the biological mechanisms regulating blood lipids to guide future genetic, biological and therapeutic research.
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| 6. |
- Badam, Tejaswi, et al.
(författare)
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CD4(+) T-cell DNA methylation changes during pregnancy significantly correlate with disease-associated methylation changes in autoimmune diseases
- 2022
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Ingår i: Epigenetics. - : Taylor & Francis Group. - 1559-2294 .- 1559-2308. ; 17:9, s. 1040-1055
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Tidskriftsartikel (refereegranskat)abstract
- Epigenetics may play a central, yet unexplored, role in the profound changes that the maternal immune system undergoes during pregnancy and could be involved in the pregnancy-induced modulation of several autoimmune diseases. We investigated changes in the methylome in isolated circulating CD4(+) T-cells in non-pregnant and pregnant women, during the 1(st) and 2(nd) trimester, using the Illumina Infinium Human Methylation 450K array, and explored how these changes were related to autoimmune diseases that are known to be affected during pregnancy. Pregnancy was associated with several hundreds of methylation differences, particularly during the 2(nd) trimester. A network-based modular approach identified several genes, e.g., CD28, FYN, VAV1 and pathways related to T-cell signalling and activation, highlighting T-cell regulation as a central component of the observed methylation alterations. The identified pregnancy module was significantly enriched for disease-associated methylation changes related to multiple sclerosis, rheumatoid arthritis and systemic lupus erythematosus. A negative correlation between pregnancy-associated methylation changes and disease-associated changes was found for multiple sclerosis and rheumatoid arthritis, diseases that are known to improve during pregnancy whereas a positive correlation was found for systemic lupus erythematosus, a disease that instead worsens during pregnancy. Thus, the directionality of the observed changes is in line with the previously observed effect of pregnancy on disease activity. Our systems medicine approach supports the importance of the methylome in immune regulation of T-cells during pregnancy. Our findings highlight the relevance of using pregnancy as a model for understanding and identifying disease-related mechanisms involved in the modulation of autoimmune diseases.
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| 7. |
- Badam, Tejaswi V. S., et al.
(författare)
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A validated generally applicable approach using the systematic assessment of disease modules by GWAS reveals a multi-omic module strongly associated with risk factors in multiple sclerosis
- 2021
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Ingår i: BMC Genomics. - : BioMed Central. - 1471-2164. ; 22:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: There exist few, if any, practical guidelines for predictive and falsifiable multi-omic data integration that systematically integrate existing knowledge. Disease modules are popular concepts for interpreting genome-wide studies in medicine but have so far not been systematically evaluated and may lead to corroborating multi-omic modules. Result: We assessed eight module identification methods in 57 previously published expression and methylation studies of 19 diseases using GWAS enrichment analysis. Next, we applied the same strategy for multi-omic integration of 20 datasets of multiple sclerosis (MS), and further validated the resulting module using both GWAS and risk-factor-associated genes from several independent cohorts. Our benchmark of modules showed that in immune-associated diseases modules inferred from clique-based methods were the most enriched for GWAS genes. The multi-omic case study using MS data revealed the robust identification of a module of 220 genes. Strikingly, most genes of the module were differentially methylated upon the action of one or several environmental risk factors in MS (n = 217, P = 10− 47) and were also independently validated for association with five different risk factors of MS, which further stressed the high genetic and epigenetic relevance of the module for MS. Conclusions: We believe our analysis provides a workflow for selecting modules and our benchmark study may help further improvement of disease module methods. Moreover, we also stress that our methodology is generally applicable for combining and assessing the performance of multi-omic approaches for complex diseases.
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| 8. |
- Badam, Tejaswi Venkata Satya, 1989-
(författare)
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Omic Network Modules in Complex diseases
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Biological systems encompass various molecular entities such as genes, proteins, and other biological molecules, including interactions among those components. Understanding a given phenotype, the functioning of a cell or tissue, aetiology of disease, or cellular organization, requires accurate measurements of the abundance profiles of these molecular entities in the form of biomedical data. The analysis of the interplay between these different entities at various levels represented in the form of biological network provides a mechanistic understanding of the observed phenotype. In order to study this interplay, there is a requirement of a conceptual and intuitive framework which can model multiple omics such as genome, transcriptome, or a proteome. This can be addressed by application of network-based strategies.Translational bioinformatics deals with the development of analytic and interpretive methods to optimize the transformation of different omics and clinical data to understanding of complex diseases and improving human health. Complex diseases such as multiple sclerosis (MS), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), and non-small cell lung cancer (NSCLC) etc., are hypothesized to be a result of a disturbance in the omic networks rendering the healthy cells to be in a state of malfunction. Even though there are numerous methods to layout the relation of the interactions among omics in complex diseases, the output network modules were not clearly interpreted.In this PhD thesis, we showed how different omic data such as transcriptome and methylome can be mapped to the network of interactions to extract highly interconnected gene sets relevant to the disease, so called disease modules. First, we selected common module identification methods and assembled them into a unified framework of the methods implemented in an Rpackage MODifieR (Paper I). Secondly, we showed that the concept of the network modules can be applied on the whole genome sequencing data for developing a tested model for predicting myelosuppressive toxicity (Paper II).Furthermore, we demonstrated that network modules extracted using the methylome data helped identifying several genes that were associated with pregnancy-induced pathways and were enriched for disease-associated methylation changes that were also shared by three auto-immune and inflammatory diseases, namely MS, RA, and SLE (Paper III). Remarkably, those methylation changes correlated with the expected outcome from clinical experience in those diseases. Last, we benchmarked the omic network modules on 19 different complex diseases using both transcriptomic and methylomic data. This led to the identification of a multi-omic MS module that was highly enriched disease-associated genes identified by genome-wide association studies, but also genes associated with the most common environmental risk factors of MS (Paper IV).The application of the network modules concept on different omics is the centrepiece of the research presented in this PhD thesis. The thesis represents the application of omic network modules in complex diseases and how these modules should be integrated and interpreted. In particular, it aimed to show the importance of networks owing to the incomplete knowledge of the genes dysregulated in complex diseases and the contribution of this thesis that provides tools and benchmarks for the methods as well as insights into how a network module can be extracted and interpreted from the omic data in complex diseases.
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| 9. |
- Barrenäs, Fredrik, et al.
(författare)
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Disease-Associated MRNA Expression Differences in Genes with Low DNA Methylation
- 2012
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Although the importance of DNA methylation for mRNA expression has been shown for individualgenes in several complex diseases, such a relation has been difficult to show on a genome-wide scale.Here, we used microarrays to examine the relationship between DNA methylation and mRNAexpression in CD4+ T cells from patients with seasonal allergic rhinitis (SAR) and healthy controls.SAR is an optimal disease model because the disease process can be studied by comparing allergenchallengedCD4+ T cells obtained from patients and controls, and mimicked in Th2 polarised T cellsfrom healthy controls. The cells from patients can be analyzed to study relations between methylationand mRNA expression, while the Th2 cells can be used for functional studies. We found that DNAmethylation, but not mRNA expression clearly separated patients from controls. Similar to studies ofother complex diseases, we found no general relation between DNA methylation and mRNAexpression. However, when we took into account the absence or presence of CpG islands in thepromoters of disease associated genes an association was found: low methylation genes without CpGislands had significantly higher expression levels of disease-associated genes. This association wasconfirmed for genes whose expression levels were regulated by a transcription factor of knownrelevance for allergy, IRF4, using combined ChIP-chip and siRNA mediated silencing of IRF4expression. In summary, disease-associated increases of mRNA expression were found in lowmethylation genes without CpG islands in CD4+ T cells from patients with SAR. Further studies arewarranted to examine if a similar association is found in other complex diseases.
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| 10. |
- Bensberg, Maike, et al.
(författare)
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TET2 as a tumor suppressor and therapeutic target in T-cell acute lymphoblastic leukemia
- 2021
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Ingår i: Proceedings of the National Academy of Sciences of the United States of America. - : National Academy of Sciences. - 0027-8424 .- 1091-6490. ; 118:34
-
Tidskriftsartikel (refereegranskat)abstract
- Pediatric T-cell acute lymphoblastic leukemia (T-ALL) is an aggres-sive malignancy resulting from overproduction of immature T-cells in the thymus and is typified by widespread alterations in DNA methyl-ation. As survival rates for relapsed T-ALL remain dismal (10 to 25%), development of targeted therapies to prevent relapse is key to improv-ing prognosis. Whereas mutations in the DNA demethylating enzyme TET2 are frequent in adult T-cell malignancies, TET2 mutations in T-ALL are rare. Here, we analyzed RNA-sequencing data of 321 primary T-ALLs, 20 T-ALL cell lines, and 25 normal human tissues, revealing that TET2 is transcriptionally repressed or silenced in 71% and 17% of T-ALL, respec-tively. Furthermore, we show that TET2 silencing is often associated with hypermethylation of the TET2 promoter in primary T-ALL. Impor-tantly, treatment with the DNA demethylating agent, 5-azacytidine (5-aza), was significantly more toxic to TET2-silenced T-ALL cells and resulted in stable re-expression of the TET2 gene. Additionally, 5-aza led to up-regulation of methylated genes and human endogenous ret-roviruses (HERVs), which was further enhanced by the addition of phys-iological levels of vitamin C, a potent enhancer of TET activity. Together, our results clearly identify 5-aza as a potential targeted therapy for TET2-silenced T-ALL.
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| 11. |
- Björn, Niclas, 1990-, et al.
(författare)
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Whole-genome sequencing and gene network modules predict gemcitabine/carboplatin-induced myelosuppression in non-small cell lung cancer patients
- 2020
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Ingår i: npj Systems Biology and Applications. - : Nature Publishing Group. - 2056-7189. ; 6:1
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Tidskriftsartikel (refereegranskat)abstract
- Gemcitabine/carboplatin chemotherapy commonly induces myelosuppression, including neutropenia, leukopenia, and thrombocytopenia. Predicting patients at risk of these adverse drug reactions (ADRs) and adjusting treatments accordingly is a long-term goal of personalized medicine. This study used whole-genome sequencing (WGS) of blood samples from 96 gemcitabine/carboplatin-treated non-small cell lung cancer (NSCLC) patients and gene network modules for predicting myelosuppression. Association of genetic variants in PLINK found 4594, 5019, and 5066 autosomal SNVs/INDELs with p ≤ 1 × 10−3 for neutropenia, leukopenia, and thrombocytopenia, respectively. Based on the SNVs/INDELs we identified the toxicity module, consisting of 215 unique overlapping genes inferred from MCODE-generated gene network modules of 350, 345, and 313 genes, respectively. These module genes showed enrichment for differentially expressed genes in rat bone marrow, human bone marrow, and human cell lines exposed to carboplatin and gemcitabine (p < 0.05). Then using 80% of the patients as training data, random LASSO reduced the number of SNVs/INDELs in the toxicity module into a feasible prediction model consisting of 62 SNVs/INDELs that accurately predict both the training and the test (remaining 20%) data with high (CTCAE 3–4) and low (CTCAE 0–1) maximal myelosuppressive toxicity completely, with the receiver-operating characteristic (ROC) area under the curve (AUC) of 100%. The present study shows how WGS, gene network modules, and random LASSO can be used to develop a feasible and tested model for predicting myelosuppressive toxicity. Although the proposed model predicts myelosuppression in this study, further evaluation in other studies is required to determine its reproducibility, usability, and clinical effect.
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| 12. |
- Björnsson, Bergthor, et al.
(författare)
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Digital twins to personalize medicine
- 2020
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Ingår i: Genome Medicine. - : Springer Science and Business Media LLC. - 1756-994X. ; 12:1
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Forskningsöversikt (refereegranskat)abstract
- Personalized medicine requires the integration and processing of vast amounts of data. Here, we propose a solution to this challenge that is based on constructing Digital Twins. These are high-resolution models of individual patients that are computationally treated with thousands of drugs to find the drug that is optimal for the patient.
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| 13. |
- Bruhn, Sören, et al.
(författare)
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A Generally Applicable Translational Strategy Identifies S100A4 as a Candidate Gene in Allergy
- 2014
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Ingår i: Science Translational Medicine. - : American Association for the Advancement of Science (AAAS). - 1946-6234 .- 1946-6242. ; 6:218
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Tidskriftsartikel (refereegranskat)abstract
- The identification of diagnostic markers and therapeutic candidate genes in common diseases is complicated by the involvement of thousands of genes. We hypothesized that genes co-regulated with a key gene in allergy, IL13, would form a module that could help to identify candidate genes. We identified a T helper 2 (T(H)2) cell module by small interfering RNA-mediated knockdown of 25 putative IL13-regulating transcription factors followed by expression profiling. The module contained candidate genes whose diagnostic potential was supported by clinical studies. Functional studies of human TH2 cells as well as mouse models of allergy showed that deletion of one of the genes, S100A4, resulted in decreased signs of allergy including TH2 cell activation, humoral immunity, and infiltration of effector cells. Specifically, dendritic cells required S100A4 for activating T cells. Treatment with an anti-S100A4 antibody resulted in decreased signs of allergy in the mouse model as well as in allergen-challenged T cells from allergic patients. This strategy, which may be generally applicable to complex diseases, identified and validated an important diagnostic and therapeutic candidate gene in allergy.
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| 14. |
- Bruhn, Sören, 1976-, et al.
(författare)
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Combining gene expression microarray- and cluster analysis with sequence-based predictions to identify regulators of IL-13 in allergy
- 2012
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Ingår i: Cytokine. - : Elsevier. - 1043-4666 .- 1096-0023. ; 60:3, s. 736-740
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Tidskriftsartikel (refereegranskat)abstract
- The Th2 cytokine IL-13 plays a key role in allergy, by regulating IgE, airway hyper secretion, eosinophils and mast cells. In this study, we aimed to identify novel transcription factors (TFs) that potentially regulated IL-13. We analyzed Th2 polarized naïve T cells from four different blood donors with gene expression microarrays to find clusters of genes that were correlated or anti-correlated with IL13. These clusters were further filtered, by selecting genes that were functionally related. In these clusters, we identified three transcription factors (TFs) that were predicted to regulate the expression of IL13, namely CEBPB, E2F6 and AHR. siRNA mediated knockdowns of these TFs in naïve polarized T cells showed significant increases of IL13, following knockdown of CEBPB and E2F6, but not AHR. This suggested an inhibitory role of CEBPB and E2F6 in the regulation of IL13 and allergy. This was supported by analysis of E2F6, but not CEBPB, in allergen-challenged CD4+ T cells from six allergic patients and six healthy controls, which showed decreased expression of E2F6 in patients. In summary, our findings indicate an inhibitory role of E2F6 in the regulation of IL-13 and allergy. The analytical approach may be generally applicable to elucidate the complex regulatory patterns in Th2 cell polarization and allergy.
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| 15. |
- Carlström, Karl E., et al.
(författare)
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Therapeutic efficacy of dimethyl fumarate in relapsing-remitting multiple sclerosis associates with ROS pathway in monocytes
- 2019
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Ingår i: Nature Communications. - : Nature Publishing Group. - 2041-1723. ; 10:1, s. 1-13
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Tidskriftsartikel (refereegranskat)abstract
- Dimethyl fumarate (DMF) is a first-line-treatment for relapsing-remitting multiple sclerosis (RRMS). The redox master regulator Nrf2, essential for redox balance, is a target of DMF, but its precise therapeutic mechanisms of action remain elusive. Here we show impact of DMF on circulating monocytes and T cells in a prospective longitudinal RRMS patient cohort. DMF increases the level of oxidized isoprostanes in peripheral blood. Other observed changes, including methylome and transcriptome profiles, occur in monocytes prior to T cells. Importantly, monocyte counts and monocytic ROS increase following DMF and distinguish patients with beneficial treatment-response from non-responders. A single nucleotide polymorphism in the ROS-generating NOX3 gene is associated with beneficial DMF treatment-response. Our data implicate monocyte-derived oxidative processes in autoimmune diseases and their treatment, and identify NOX3 genetic variant, monocyte counts and redox state as parameters potentially useful to inform clinical decisions on DMF therapy of RRMS.
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| 16. |
- Das, Jyotirmoy, et al.
(författare)
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Identification of DNA methylation patterns predisposing for an efficient response to BCG vaccination in healthy BCG-naive subjects
- 2019
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Ingår i: Epigenetics. - : TAYLOR & FRANCIS INC. - 1559-2294 .- 1559-2308. ; 14:6, s. 589-601
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Tidskriftsartikel (refereegranskat)abstract
- The protection against tuberculosis induced by the Bacille Calmette Guerin (BCG) vaccine is unpredictable. In our previous study, altered DNA methylation pattern in peripheral blood mononuclear cells (PBMCs) in response to BCG was observed in a subgroup of individuals, whose macrophages killed mycobacteria effectively (responders). These macrophages also showed production of Interleukin-1 beta (IL-1 beta) in response to mycobacterial stimuli before vaccination. Here, we hypothesized that the propensity to respond to the BCG vaccine is reflected in the DNA methylome. We mapped the differentially methylated genes (DMGs) in PBMCs isolated from responders/non-responders at the time point before vaccination aiming to identify possible predictors of BCG responsiveness. We identified 43 DMGs and subsequent bioinformatic analyses showed that these were enriched for actin-modulating pathways, predicting differences in phagocytosis. This could be validated by experiments showing that phagocytosis of mycobacteria, which is an event preceding mycobacteria-induced IL-1 beta production, was strongly correlated with the DMG pattern.
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| 17. |
- de Weerd, Hendrik A., et al.
(författare)
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MODalyseR—a novel software for inference of disease module hub regulators identified a putative multiple sclerosis regulator supported by independent eQTL data
- 2022
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Ingår i: Bioinformatics Advances. - : Oxford University Press. - 2635-0041. ; 2:1
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Tidskriftsartikel (refereegranskat)abstract
- MotivationNetwork-based disease modules have proven to be a powerful concept for extracting knowledge about disease mechanisms, predicting for example disease risk factors and side effects of treatments. Plenty of tools exist for the purpose of module inference, but less effort has been put on simultaneously utilizing knowledge about regulatory mechanisms for predicting disease module hub regulators.ResultsWe developed MODalyseR, a novel software for identifying disease module regulators and reducing modules to the most disease-associated genes. This pipeline integrates and extends previously published software packages MODifieR and ComHub and hereby provides a user-friendly network medicine framework combining the concepts of disease modules and hub regulators for precise disease gene identification from transcriptomics data. To demonstrate the usability of the tool, we designed a case study for multiple sclerosis that revealed IKZF1 as a promising hub regulator, which was supported by independent ChIP-seq data.Availability and implementationMODalyseR is available as a Docker image at https://hub.docker.com/r/ddeweerd/modalyser with user guide and installation instructions found at https://gustafsson-lab.gitlab.io/MODalyseR/.Supplementary informationSupplementary data are available at Bioinformatics Advances online.
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| 18. |
- de Weerd, Hendrik A., et al.
(författare)
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MODifieR : an ensemble R package for inference of disease modules from transcriptomics networks
- 2020
-
Ingår i: Bioinformatics. - : Oxford University Press. - 1367-4803 .- 1367-4811 .- 1460-2059. ; 36:12, s. 3918-3919
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Tidskriftsartikel (refereegranskat)abstract
- MOTIVATION: Complex diseases are due to the dense interactions of many disease-associated factors that dysregulate genes that in turn form so-called disease modules, which have shown to be a powerful concept for understanding pathological mechanisms. There exist many disease module inference methods that rely on somewhat different assumptions, but there is still no gold standard or best performing method. Hence, there is a need for combining these methods to generate robust disease modules.RESULTS: We developed MODule IdentiFIER (MODifieR), an ensemble R package of nine disease module inference methods from transcriptomics networks. MODifieR uses standardized input and output allowing the possibility to combine individual modules generated from these methods into more robust disease-specific modules, contributing to a better understanding of complex diseases.AVAILABILITY: MODifieR is available under the GNU GPL license and can be freely downloaded from https://gitlab.com/Gustafsson-lab/MODifieR and as a Docker image from https://hub.docker.com/r/ddeweerd/modifier.SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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| 19. |
- de Weerd, Hendrik Arnold, 1986-
(författare)
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Novel methods and software for disease module inference
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Cellular organization is believed to be modular, meaning cellular functions are carried out by modules composed of clusters of genes, proteins and metabolites that are interconnected, co-regulated or physically interacting. In turn, these modules interact together and thereby form complex networks that taken together is considered to be the interactome. Modern high-throughput biological techniques have made high-scale accurate quantification of these biological molecules possible, the so called omics. The simultaneous measurement of these molecules enables a picture of the state of a cell at a resolution that was never before possible. Mapping these measurements aids greatly to elucidate a network structure of interactions. The ever growing size of public repositories for omics data has ushered in the advent of biology as a (big) data science and opens the door for data hungry machine learning approaches in biology. Complex diseases are multi-factorial and arise from a combination of genetic, environmental and lifestyle factors. Additionally, diagnosis and treatment is complicated by the fact that these genetic, environmental and lifestyle factors can vary between patients and may or may not give rise to different disease phenotypes that still classify as the same disease. Genetically, there is substantial heterogeneity among patients and therefore the emergence of a disease phenotype cannot be attributed to a single genetic mutation but rather to a combination of various mutations that may vary from patient to patient. As complex diseases can have different root causes but give rise to a similar disease phenotype, the implication is that different root causes perturb similar components in the interactome. Most of the work in this thesis is aimed at developing methods and computational pipelines to identify, analyze and evaluate these perturbed disease specific sub-networks in the interactome, so called disease modules. We started by collecting popular disease module inference methods and combined them in a unified framework, an R package called MODifieR (Paper I). The package uses standardized inputs and outputs, allowing for a more user-friendly way of running multiple disease module inference methods and the combining of modules. Next, we benchmarked the MODifieR methods on a compendium of transcriptomic and methylomic datasets and combined transcriptomic and methylomic disease modules for Multiple Sclerosis (MS) to a highly disease-relevant module greatly enriched with known risk factors for MS (Paper II). Subsequently, we extended the functionality of MODifieR with software for transcription factor hub detection in gene regulatory networks in a new framework with a graphical user interface, MODalyseR. We used MODalyseR to find upstream regulators and identified IKZF1 as an important upstream regulator for MS (Paper III). Lastly, we used the growing large-scale repositories of gene expression data to train a Variational Auto Encoder (VAE) to compress and decompress gene expression profiles with the aim of extracting disease modules from the latent space. Utilizing the continues nature of the latent space in VAE’s, we derived the differences in latent space representations between a compendium of complex disease gene expression profiles and matched healthy controls. We then derived disease modules from the decompressed latent space representation of this difference and found the modules highly enriched with disease-associated genes, generally outperforming the gold standard of transcriptomic analysis of diseases, top differentially expressed genes (Paper IV). To conclude, the main scientific contribution of this thesis lies in the development of software and methods for improving disease module inference, the evaluation of existing inference methods, the creation of new analysis workflows for multi-omics modules, and the introduction of a deep learning-based approach to the disease module inference toolkit.
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| 20. |
- Dwivedi, Sanjiv, et al.
(författare)
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Deriving disease modules from the compressed transcriptional space embedded in a deep autoencoder
- 2020
-
Ingår i: Nature Communications. - : NATURE PUBLISHING GROUP. - 2041-1723. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- Disease modules in molecular interaction maps have been useful for characterizing diseases. Yet biological networks, that commonly define such modules are incomplete and biased toward some well-studied disease genes. Here we ask whether disease-relevant modules of genes can be discovered without prior knowledge of a biological network, instead training a deep autoencoder from large transcriptional data. We hypothesize that modules could be discovered within the autoencoder representations. We find a statistically significant enrichment of genome-wide association studies (GWAS) relevant genes in the last layer, and to a successively lesser degree in the middle and first layers respectively. In contrast, we find an opposite gradient where a modular protein-protein interaction signal is strongest in the first layer, but then vanishing smoothly deeper in the network. We conclude that a data-driven discovery approach is sufficient to discover groups of disease-related genes. The study of disease modules facilitates insight into complex diseases, but their identification relies on knowledge of molecular networks. Here, the authors show that disease modules and genes can also be discovered in deep autoencoder representations of large human gene expression datasets.
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| 21. |
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| 22. |
- Edström, Måns, et al.
(författare)
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Regulatory T cells in Multiple Sclerosis – Indications of impaired function of suppressive capacity and a role for chemokines
- 2014
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- BACKGROUND Regulatory T cells (Treg) are critical for immune regulation and homeostasis. In multiple sclerosis (MS), the function of these cells has been shown to be impaired, although the underlying mechanism has yet to be shown. In the current study, we aimed to characterize and assess the phenotypical, functional and transcriptional characteristics of memory and naïve Treg in MS patients and controls.MATERIAL AND METHODS 27 patients with relapsing-remitting disease were included, along with 29 healthy controls. Flow cytometry was used for detailed phenotyping of Treg subpopulations CD4+CD45RA+/- and CD4dimCD25++ and their expression of FOXP3, CD39 and HELIOS. CFSE (proliferation marker) and CD69 (activation marker) were used to investigate the functional capacity of Treg. A microarray was employed for genome-wide transcriptional characterization of isolated Treg.RESULTS CD4+CD45RA–CD25++ activated Treg displayed a higher expression of FOXP3 and CD39 than resting CD4+CD45RA+CD25+ Treg, while no significant phenotypical differences were observed in Treg subpopulations between patients and controls. However, a lower anti-proliferative capacity was observed in activated Treg of MS patients compared with those of controls (p<0.05), while suppression of activation was similar to controls. Gene set enrichment analysis (GSEA) of microarray data revealed enrichment for the GO gene set ‘chemokine receptor binding’ in MS Treg.CONCLUSION Although numerical phenotypical assessment of resting and activated Tregs did not reveal any significant difference between patients and controls, functional co-culturing experiments showed an impaired function in activated Treg of MS patients. Furthermore, GSEA revealed immune-related gene sets overexpressed in Treg of MS patients, possibly containing clues to the functional impairment. In particular over-activity in chemokine signalling in Treg would be of interest for further investigation.
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| 23. |
- Folkersen, Lasse, et al.
(författare)
-
Genomic and drug target evaluation of 90 cardiovascular proteins in 30,931 individuals.
- 2020
-
Ingår i: Nature metabolism. - : Springer Science and Business Media LLC. - 2522-5812. ; 2:10, s. 1135-1148
-
Tidskriftsartikel (refereegranskat)abstract
- Circulating proteins are vital in human health and disease and are frequently used as biomarkers for clinical decision-making or as targets for pharmacological intervention. Here, we map and replicate protein quantitative trait loci (pQTL) for 90 cardiovascular proteins in over 30,000 individuals, resulting in 451 pQTLs for 85 proteins. For each protein, we further perform pathway mapping to obtain trans-pQTL gene and regulatory designations. We substantiate these regulatory findings with orthogonal evidence for trans-pQTLs using mouse knockdown experiments (ABCA1 and TRIB1) and clinical trial results (chemokine receptors CCR2 and CCR5), with consistent regulation. Finally, we evaluate known drug targets, and suggest new target candidates or repositioning opportunities using Mendelian randomization. This identifies 11 proteins with causal evidence of involvement in human disease that have not previously been targeted, including EGF, IL-16, PAPPA, SPON1, F3, ADM, CASP-8, CHI3L1, CXCL16, GDF15 and MMP-12. Taken together, these findings demonstrate the utility of large-scale mapping of the genetics of the proteome and provide a resource for future precision studies of circulating proteins in human health.
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| 24. |
- Forsberg, Anna, et al.
(författare)
-
Pre- and postnatal Lactobacillus reuteri treatment alters DNA methylation of infant T helper cells
- 2020
-
Ingår i: Pediatric Allergy and Immunology. - : WILEY. - 0905-6157 .- 1399-3038. ; 31:5, s. 544-553
-
Tidskriftsartikel (refereegranskat)abstract
- Background Perinatal childhood exposures, including probiotic supplementation, may affect epigenetic modifications and impact on immune maturation and allergy development. The aim of this study was to assess the effects of pre- and postnatal Lactobacillus reuteri supplementation on DNA methylation in relation to immune maturation and allergy development. Methods DNA methylation patterns were investigated for allergy-related T helper subsets using a locus-specific method and at a genome-wide scale using the Illumina 450K array. From a randomised, double-blind, placebo-controlled allergy prevention trial with pre- and postnatal probiotic supplementation, CD4+ T helper cells were obtained at birth (from cord blood), and 12 and 24 months of age (total (placebo/probiotics); locus-specific method: CB = 32 (17/15), 12 months = 24 (9/15), 24 months = 35 (15/20); Illumina: CB = 19 (10/9), 12 months = 10 (6/4), 24 months = 19(11/8)). Results Comparing probiotics to placebo, the greatest genome-wide differential DNA methylation was observed at birth, where the majority of sites were hypomethylated, indicating transcriptional accessibility in the probiotic group. Bioinformatic analyses, including network analyses, revealed a module containing 91 genes, enriched for immune-related pathways such as chemotaxis, PI3K-Akt, MAPK and TGF-beta signalling. A majority of the module genes were associated with atopic manifestations (OR = 1.43, P = 2.4 x 10(-6)), and a classifier built on this model could predict allergy development (AUC = 0.78, P = 3.0 x 10(e-3)). Pathways such as IFN-gamma signalling and T-cell activation were more hypermethylated at birth compared with later in life in both intervention groups over time, in line with DNA methylation patterns in the IFNG locus obtained by the locus-specific methodology. Conclusion Maternal L. reuteri supplementation during pregnancy alters DNA methylation patterns in CD4+ T cells towards enhanced immune activation at birth, which may affect immune maturation and allergy development.
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| 25. |
- Gawel, Danuta, et al.
(författare)
-
A validated single-cell-based strategy to identify diagnostic and therapeutic targets in complex diseases
- 2019
-
Ingår i: Genome Medicine. - : Springer Science and Business Media LLC. - 1756-994X. ; 11
-
Tidskriftsartikel (refereegranskat)abstract
- Background: Genomic medicine has paved the way for identifying biomarkers and therapeutically actionable targets for complex diseases, but is complicated by the involvement of thousands of variably expressed genes across multiple cell types. Single-cell RNA-sequencing study (scRNA-seq) allows the characterization of such complex changes in whole organs. Methods: The study is based on applying network tools to organize and analyze scRNA-seq data from a mouse model of arthritis and human rheumatoid arthritis, in order to find diagnostic biomarkers and therapeutic targets. Diagnostic validation studies were performed using expression profiling data and potential protein biomarkers from prospective clinical studies of 13 diseases. A candidate drug was examined by a treatment study of a mouse model of arthritis, using phenotypic, immunohistochemical, and cellular analyses as read-outs. Results: We performed the first systematic analysis of pathways, potential biomarkers, and drug targets in scRNA-seq data from a complex disease, starting with inflamed joints and lymph nodes from a mouse model of arthritis. We found the involvement of hundreds of pathways, biomarkers, and drug targets that differed greatly between cell types. Analyses of scRNA-seq and GWAS data from human rheumatoid arthritis (RA) supported a similar dispersion of pathogenic mechanisms in different cell types. Thus, systems-level approaches to prioritize biomarkers and drugs are needed. Here, we present a prioritization strategy that is based on constructing network models of disease-associated cell types and interactions using scRNA-seq data from our mouse model of arthritis, as well as human RA, which we term multicellular disease models (MCDMs). We find that the network centrality of MCDM cell types correlates with the enrichment of genes harboring genetic variants associated with RA and thus could potentially be used to prioritize cell types and genes for diagnostics and therapeutics. We validated this hypothesis in a large-scale study of patients with 13 different autoimmune, allergic, infectious, malignant, endocrine, metabolic, and cardiovascular diseases, as well as a therapeutic study of the mouse arthritis model. Conclusions: Overall, our results support that our strategy has the potential to help prioritize diagnostic and therapeutic targets in human disease.
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| 26. |
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| 27. |
- Gawel, Danuta R., 1988-
(författare)
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Identification of genes and regulators that are shared across T cell associated diseases
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Genome-wide association studies (GWASs) of hundreds of diseases and millions of patients have led to the identification of genes that are associated with more than one disease. The aims of this PhD thesis were to a) identify a group of genes important in multiple diseases (shared disease genes), b) identify shared up-stream disease regulators, and c) determine how the same genes can be involved in the pathogenesis of different diseases. These aims have been tested on CD4+ T cells because they express the T helper cell differentiation pathway, which was the most enriched pathway in analyses of all disease associated genes identified with GWASs.Combining information about known gene-gene interactions from the protein-protein interaction (PPI) network with gene expression changes in multiple T cell associated diseases led to the identification of a group of highly interconnected genes that were miss-expressed in many of those diseases – hereafter called ‘shared disease genes’. Those genes were further enriched for inflammatory, metabolic and proliferative pathways, genetic variants identified by all GWASs, as well as mutations in cancer studies and known diagnostic and therapeutic targets. Taken together, these findings supported the relevance of the shared disease genes.Identification of the shared upstream disease regulators was addressed in the second project of this PhD thesis. The underlying hypothesis assumed that the determination of the shared upstream disease regulators is possible through a network model showing in which order genes activate each other. For that reason a transcription factor–gene regulatory network (TF-GRN) was created. The TF-GRN was based on the time-series gene expression profiling of the T helper cell type 1 (Th1), and T helper cell type 2 (Th2) differentiation from Native T-cells. Transcription factors (TFs) whose expression changed early during polarization and had many downstream predicted targets (hubs) that were enriched for disease associated single nucleotide polymorphisms (SNPs) were prioritised as the putative early disease regulators. These analyses identified three transcription factors: GATA3, MAF and MYB. Their predicted targets were validated by ChIP-Seq and siRNA mediated knockdown in primary human T-cells. CD4+ T cells isolated from seasonal allergic rhinitis (SAR) and multiple sclerosis (MS) patients in their non-symptomatic stages were analysed in order to demonstrate predictive potential of those three TFs. We found that those three TFs were differentially expressed in symptom-free stages of the two diseases, while their TF-GRN{predicted targets were differentially expressed during symptomatic disease stages. Moreover, using RNA-Seq data we identified a disease associated SNP that correlated with differential splicing of GATA3.A limitation of the above study is that it concentrated on TFs as main regulators in cells, excluding other potential regulators such as microRNAs. To this end, a microRNA{gene regulatory network (mGRN) of human CD4+ T cell differentiation was constructed. Within this network, we defined regulatory clusters (groups of microRNAs that are regulating groups of mRNAs). One regulatory cluster was differentially expressed in all of the tested diseases, and was highly enriched for GWAS SNPs. Although the microRNA processing machinery was dynamically upregulated during early T-cell activation, the majority of microRNA modules showed specialisation in later time-points.In summary this PhD thesis shows the relevance of shared genes and up-stream disease regulators. Putative mechanisms of why shared genes can be involved in pathogenesis of different diseases have also been demonstrated: a) differential gene expression in different diseases; b) alternative transcription factor splicing variants may affect different downstream gene target group; and c) SNPs might cause alternative splicing.
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| 28. |
- Gawel, Danuta, et al.
(författare)
-
The Allergic Airway Inflammation Repository - a user-friendly, curated resource of mRNA expression levels in studies of allergic airways
- 2014
-
Ingår i: Allergy. European Journal of Allergy and Clinical Immunology. - : Wiley. - 0105-4538 .- 1398-9995. ; 69:8, s. 1115-1117
-
Tidskriftsartikel (refereegranskat)abstract
- Public microarray databases allow analysis of expression levels of candidate genes in different contexts. However, finding relevant microarray data is complicated by the large number of available studies. We have compiled a user-friendly, open-access database of mRNA microarray experiments relevant to allergic airway inflammation, the Allergic Airway Inflammation Repository (AAIR, http://aair.cimed.ike.liu.se/). The aim is to allow allergy researchers to determine the expression profile of their genes of interest in multiple clinical data sets and several experimental systems quickly and intuitively. AAIR also provides quick links to other relevant information such as experimental protocols, related literature and raw data files.
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| 29. |
- Gustafsson, Mika, et al.
(författare)
-
A validated gene regulatory network and GWAS identifies early regulators of T cell-associated diseases
- 2015
-
Ingår i: Science Translational Medicine. - : AMER ASSOC ADVANCEMENT SCIENCE. - 1946-6234 .- 1946-6242. ; 7:313
-
Tidskriftsartikel (refereegranskat)abstract
- Early regulators of disease may increase understanding of disease mechanisms and serve as markers for presymptomatic diagnosis and treatment. However, early regulators are difficult to identify because patients generally present after they are symptomatic. We hypothesized that early regulators of T cell-associated diseases could be found by identifying upstream transcription factors (TFs) in T cell differentiation and by prioritizing hub TFs that were enriched for disease-associated polymorphisms. A gene regulatory network (GRN) was constructed by time series profiling of the transcriptomes and methylomes of human CD4(+) T cells during in vitro differentiation into four helper T cell lineages, in combination with sequence-based TF binding predictions. The TFs GATA3, MAF, and MYB were identified as early regulators and validated by ChIP-seq (chromatin immunoprecipitation sequencing) and small interfering RNA knockdowns. Differential mRNA expression of the TFs and their targets in T cell-associated diseases supports their clinical relevance. To directly test if the TFs were altered early in disease, T cells from patients with two T cell-mediated diseases, multiple sclerosis and seasonal allergic rhinitis, were analyzed. Strikingly, the TFs were differentially expressed during asymptomatic stages of both diseases, whereas their targets showed altered expression during symptomatic stages. This analytical strategy to identify early regulators of disease by combining GRNs with genome-wide association studies may be generally applicable for functional and clinical studies of early disease development.
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| 30. |
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| 31. |
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| 32. |
- Gustafsson, Mika, 1977-, et al.
(författare)
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Comparison and validation of community structures in complex networks
- 2006
-
Ingår i: Physica A. - : Elsevier BV. - 0378-4371 .- 1873-2119. ; 367, s. 559-576
-
Tidskriftsartikel (refereegranskat)abstract
- The issue of partitioning a network into communities has attracted a great deal of attention recently. Most authors seem to equate this issue with the one of finding the maximum value of the modularity, as defined by Newman. Since the problem formulated this way is believed to be NP-hard, most effort has gone into the construction of search algorithms, and less to the question of other measures of community structures, similarities between various partitionings and the validation with respect to external information.Here we concentrate on a class of computer generated networks and on three well-studied real networks which constitute a bench-mark for network studies; the karate club, the US college football teams and a gene network of yeast. We utilize some standard ways of clustering data (originally not designed for finding community structures in networks) and show that these classical methods sometimes outperform the newer ones. We discuss various measures of the strength of the modular structure, and show by examples features and drawbacks. Further, we compare different partitions by applying some graph-theoretic concepts of distance, which indicate that one of the quality measures of the degree of modularity corresponds quite well with the distance from the true partition. Finally, we introduce a way to validate the partitionings with respect to external data when the nodes are classified but the network structure is unknown. This is here possible since we know everything of the computer generated networks, as well as the historical answer to how the karate club and the football teams are partitioned in reality. The partitioning of the gene network is validated by use of the Gene Ontology database, where we show that a community in general corresponds to a biological process.
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| 33. |
- Gustafsson, Mika, 1977-, et al.
(författare)
-
Constructing and analyzing a large-scale gene-to-gene regulatory network Lasso-constrained inference and biological validation
- 2005
-
Ingår i: IEEE/ACM Transactions on Computational Biology & Bioinformatics. - 1545-5963 .- 1557-9964. ; 2:3, s. 254-261
-
Tidskriftsartikel (refereegranskat)abstract
- We construct a gene-to-gene regulatory network from time-series data of expression levels for the whole genome of the yeast Saccharomyces cerevisae, in a case where the number of measurements is much smaller than the number of genes in the network. This network is analyzed with respect to present biological knowledge of all genes (according to the Gene Ontology database), and we find some of its large-scale properties to be in accordance with known facts about the organism. The linear modeling employed here has been explored several times, but due to lack of any validation beyond investigating individual genes, it has been seriously questioned with respect to its applicability to biological systems. Our results show the adequacy of the approach and make further investigations of the model meaningful.
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| 34. |
- Gustafsson, Mika, 1977-, et al.
(författare)
-
Data for: Proteomics reveal biomarkers for diagnosis, disease activity and long-term disability outcomes in multiple sclerosis
- 2023
-
Annan publikationabstract
- Protein levels were measured in cerebrospinal fluid samples (CSF; n = 186) and plasma samples (n = 165) from persons with multiple sclerosis and healthy controls. CSF samples and plasma samples were taken from 92 persons with CIS or RRMS at Linköping University Hospital, Sweden and 51 persons with CIS or RRMS at the Karolinska University Hospital, Sweden. Everyone fulfilled the revised McDonald criteria from 2010 and 2017 for CIS or Multiple sclerosis (MS). Age-matched healthy controls (HC) were recruited from healthy blood donors (23 at the Linköping University hospital and 20 at the Karolinska University Hospital). The concentration of 1463 proteins were measured using the Olink Explore platform which uses Proximity Extension Assay (PEA) technology. The proteins were preselected from four Olink panels: Explore 384 Cardiometabolic, Explore 384 Inflammation, Explore 384 Neurology, and Explore 384 Oncology. The protein concentrations are given as Olink’s relative protein quantification unit on log2 scale: Normalized Protein Expression (NPX). The NPX values were intensity normalized by Olink.
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| 35. |
- Gustafsson, Mika, 1977-, et al.
(författare)
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Gene Expression Prediction by Soft Integration and the Elastic Net : Best Performance of the DREAM3 Gene Expression Challenge
- 2010
-
Ingår i: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 5:2, s. e9134-
-
Tidskriftsartikel (refereegranskat)abstract
- Background: To predict gene expressions is an important endeavour within computational systems biology. It can both be a way to explore how drugs affect the system, as well as providing a framework for finding which genes are interrelated in a certain process. A practical problem, however, is how to assess and discriminate among the various algorithms which have been developed for this purpose. Therefore, the DREAM project invited the year 2008 to a challenge for predicting gene expression values, and here we present the algorithm with best performance.Methodology/Principal Findings: We develop an algorithm by exploring various regression schemes with different model selection procedures. It turns out that the most effective scheme is based on least squares, with a penalty term of a recently developed form called the “elastic net”. Key components in the algorithm are the integration of expression data from other experimental conditions than those presented for the challenge and the utilization of transcription factor binding data for guiding the inference process towards known interactions. Of importance is also a cross-validation procedure where each form of external data is used only to the extent it increases the expected performance.Conclusions/Significance: Our algorithm proves both the possibility to extract information from large-scale expression data concerning prediction of gene levels, as well as the benefits of integrating different data sources for improving the inference. We believe the former is an important message to those still hesitating on the possibilities for computational approaches, while the latter is part of an important way forward for the future development of the field of computational systems biology.
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| 36. |
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| 37. |
- Gustafsson, Mika, 1977-
(författare)
-
Gene networks from high-throughput data : Reverse engineering and analysis
- 2010
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Experimental innovations starting in the 1990’s leading to the advent of high-throughput experiments in cellular biology have made it possible to measure thousands of genes simultaneously at a modest cost. This enables the discovery of new unexpected relationships between genes in addition to the possibility of falsify existing. To benefit as much as possible from these experiments the new inter disciplinary research field of systems biology have materialized. Systems biology goes beyond the conventional reductionist approach and aims at learning the whole system under the assumption that the system is greater than the sum of its parts. One emerging enterprise in systems biology is to use the high-throughput data to reverse engineer the web of gene regulatory interactions governing the cellular dynamics. This relatively new endeavor goes further than clustering genes with similar expression patterns and requires the separation of cause of gene expression from the effect. Despite the rapid data increase we then face the problem of having too few experiments to determine which regulations are active as the number of putative interactions has increased dramatic as the number of units in the system has increased. One possibility to overcome this problem is to impose more biologically motivated constraints. However, what is a biological fact or not is often not obvious and may be condition dependent. Moreover, investigations have suggested several statistical facts about gene regulatory networks, which motivate the development of new reverse engineering algorithms, relying on different model assumptions. As a result numerous new reverse engineering algorithms for gene regulatory networks has been proposed. As a consequent, there has grown an interest in the community to assess the performance of different attempts in fair trials on “real” biological problems. This resulted in the annually held DREAM conference which contains computational challenges that can be solved by the prosing researchers directly, and are evaluated by the chairs of the conference after the submission deadline.This thesis contains the evolution of regularization schemes to reverse engineer gene networks from high-throughput data within the framework of ordinary differential equations. Furthermore, to understand gene networks a substantial part of it also concerns statistical analysis of gene networks. First, we reverse engineer a genome-wide regulatory network based solely on microarray data utilizing an extremely simple strategy assuming sparseness (LASSO). To validate and analyze this network we also develop some statistical tools. Then we present a refinement of the initial strategy which is the algorithm for which we achieved best performer at the DREAM2 conference. This strategy is further refined into a reverse engineering scheme which also can include external high-throughput data, which we confirm to be of relevance as we achieved best performer in the DREAM3 conference as well. Finally, the tools we developed to analyze stability and flexibility in linearized ordinary differential equations representing gene regulatory networks is further discussed.
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| 38. |
- Gustafsson, Mika, et al.
(författare)
-
Genome-wide system analysis reveals stable yet flexible network dynamics in yeast
- 2009
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Ingår i: IET SYSTEMS BIOLOGY. - : Institution of Engineering and Technology (IET). - 1751-8849 .- 1751-8857. ; 3:4, s. 219-228
-
Tidskriftsartikel (refereegranskat)abstract
- Recently, important insights into static network topology for biological systems have been obtained, but still global dynamical network properties determining stability and system responsiveness have not been accessible for analysis. Herein, we explore a genome-wide gene-to-gene regulatory network based on expression data from the cell cycle in Saccharomyces cerevisae (budding yeast). We recover static properties like hubs (genes having several out-going connections), network motifs and modules, which have previously been derived from multiple data sources such as whole-genome expression measurements, literature mining, protein-protein and transcription factor binding data. Further, our analysis uncovers some novel dynamical design principles; hubs are both repressed and repressors, and the intra-modular dynamics are either strongly activating or repressing whereas inter-modular couplings are weak. Finally, taking advantage of the inferred strength and direction of all interactions, we perform a global dynamical systems analysis of the network. Our inferred dynamics of hubs, motifs and modules produce a more stable network than what is expected given randomised versions. The main contribution of the repressed hubs is to increase system stability, while higher order dynamic effects (e.g. module dynamics) mainly increase system flexibility. Altogether, the presence of hubs, motifs and modules induce few flexible modes, to which the network is extra sensitive to an external signal. We believe that our approach, and the inferred biological mode of strong flexibility and stability, will also apply to other cellular networks and adaptive systems.
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| 39. |
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| 40. |
- Gustafsson, Mika, et al.
(författare)
-
Integrated genomic and prospective clinical studies show the importance of modular pleiotropy for disease susceptibility, diagnosis and treatment
- 2014
-
Ingår i: Genome Medicine. - : BioMed Central. - 1756-994X. ; 6:17
-
Tidskriftsartikel (refereegranskat)abstract
- Background: Translational research typically aims to identify and functionally validate individual, disease-specific genes. However, reaching this aim is complicated by the involvement of thousands of genes in common diseases, and that many of those genes are pleiotropic, that is, shared by several diseases. Methods: We integrated genomic meta-analyses with prospective clinical studies to systematically investigate the pathogenic, diagnostic and therapeutic roles of pleiotropic genes. In a novel approach, we first used pathway analysis of all published genome-wide association studies (GWAS) to find a cell type common to many diseases. Results: The analysis showed over-representation of the T helper cell differentiation pathway, which is expressed in T cells. This led us to focus on expression profiling of CD4(+) T cells from highly diverse inflammatory and malignant diseases. We found that pleiotropic genes were highly interconnected and formed a pleiotropic module, which was enriched for inflammatory, metabolic and proliferative pathways. The general relevance of this module was supported by highly significant enrichment of genetic variants identified by all GWAS and cancer studies, as well as known diagnostic and therapeutic targets. Prospective clinical studies of multiple sclerosis and allergy showed the importance of both pleiotropic and disease specific modules for clinical stratification. Conclusions: In summary, this translational genomics study identified a pleiotropic module, which has key pathogenic, diagnostic and therapeutic roles.
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| 41. |
- Gustafsson, Mika, et al.
(författare)
-
Integrating various data sources for improved quality in reverse engineering of gene regulatory networks
- 2009. - 1
-
Ingår i: Handbook of Research on Computational Methodologies in Gene Regulatory Networks. - : IGI Global. - 9781605666853 - 9781605666860 ; , s. 476-496
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- In this chapter we outline a methodology to reverse engineer GRNs from various data sources within an ODE framework. The methodology is generally applicable and is suitable to handle the broad error distribution present in microarrays. The main effort of this chapter is the exploration of a fully data driven approach to the integration problem in a “soft evidence” based way. Integration is here seen as the process of incorporation of uncertain a priori knowledge and is therefore only relied upon if it lowers the prediction error. An efficient implementation is carried out by a linear programming formulation. This LP problem is solved repeatedly with small modifications, from which we can benefit by restarting the primal simplex method from nearby solutions, which enables a computational efficient execution. We perform a case study for data from the yeast cell cycle, where all verified genes are putative regulators and the a priori knowledge consists of several types of binding data, text-mining and annotation knowledge.
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| 42. |
- Gustafsson, Mika, 1977-
(författare)
-
Large-scale topology, stability and biology of gene networks
- 2006
-
Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Experimental innovations in cell biology have provided a huge amount of genomescale data sets, settling the stage for understanding organisms on a system level. Recently, complex networks have been widely adopted and serve as a unifying language for widely different systems, including social, technological and biological systems. Still- in most biological cases-the number of interacting units vastly exceeds the number of measurements, hence large-scale models must still be very simple or non-specific. In this thesis we explore the limits of a linear (Lasso) network model on a genomic-scale for the Saccharomyces cerevisae organism and the limits of some analysis tools from the research field of complex networks. The former study (Paper I and Paper III) mainly regards validation issues, but also stipulate novel statistical system hypotheses, e.g., the system is significantly more stable than random, but still flexible to target stimuli. The latter study (Paper II) explores different heuristics in the search for communities (i.e., dense sub-graphs) within large complex networks and how the concept relates to functional modules.
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| 43. |
- Gustafsson, Mika, et al.
(författare)
-
Modules, networks and systems medicine for understanding disease and aiding diagnosis
- 2014
-
Ingår i: Genome Medicine. - : BioMed Central. - 1756-994X. ; 6:82
-
Forskningsöversikt (refereegranskat)abstract
- Many common diseases, such as asthma, diabetes or obesity, involve altered interactions between thousands of genes. High-throughput techniques (omics) allow identification of such genes and their products, but functional understanding is a formidable challenge. Network-based analyses of omics data have identified modules of disease-associated genes that have been used to obtain both a systems level and a molecular understanding of disease mechanisms. For example, in allergy a module was used to find a novel candidate gene that was validated by functional and clinical studies. Such analyses play important roles in systems medicine. This is an emerging discipline that aims to gain a translational understanding of the complex mechanisms underlying common diseases. In this review, we will explain and provide examples of how network-based analyses of omics data, in combination with functional and clinical studies, are aiding our understanding of disease, as well as helping to prioritize diagnostic markers or therapeutic candidate genes. Such analyses involve significant problems and limitations, which will be discussed. We also highlight the steps needed for clinical implementation.
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| 44. |
- Gustafsson, Mika, et al.
(författare)
-
Prognosmodeller för tillståndsmått i Trafikverkets Pavement Management System - makrotextur, MPD
- 2012
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Vägytans tillstånd på det statliga svenska vägnätet har inventerats med mätbil sedan 1987. Syftet med dessa mätningar är att förse Trafikverkets pavement management system (PMS) med tillståndsdata. Av kostnadsskäl mäts inte alla vägar varje år, men Trafikverket har behov av en fullständig tillståndsbeskrivning av vägnätet. Ett sätt att årligen beskriva tillståndet för hela vägnätet är att använda modeller som prognostiserar tillståndet för de år då mätningar saknas. Denna rapport har två huvudsyften, dels att arbeta fram en metod och ett angreppssätt för att skapa en prognosmodell för ett tillståndsmått som kan förändras såväl linjärt som ickelinjärt, dels att, under metodutvecklingen, skapa en prognosmodell för MPD. Modellen är datadrivet genererad, men beskriver även tidsutvecklingen. Från denna kan vi se tydliga trender för MPD-utvecklingen på årsbasis. Det angreppssätt som används för att skapa en prognosmodell för MPD kan användas för andra vägtillståndsmått såsom megatextur, kantdjup, vattenarea etc.
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| 45. |
- Gustafsson, Mika, et al.
(författare)
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Reverse Engineering of Gene Networks with LASSO and Nonlinear Basis Functions
- 2009
-
Ingår i: CHALLENGES OF SYSTEMS BIOLOGY: COMMUNITY EFFORTS TO HARNESS BIOLOGICAL COMPLEXITY. - : Wiley. - 0077-8923 .- 1749-6632. ; 1158, s. 265-275
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Tidskriftsartikel (refereegranskat)abstract
- The quest to determine cause from effect is often referred to as reverse engineering in the context of cellular networks. Here we propose and evaluate an algorithm for reverse engineering a gene regulatory network from time-series kind steady-state data. Our algorithmic pipeline, which is rather standard in its parts but not in its integrative composition, combines ordinary differential equations, parameter estimations by least angle regression, and cross-validation procedures for determining the in-degrees and selection of nonlinear transfer functions. The result of the algorithm is a complete directed net-work, in which each edge has been assigned a score front it bootstrap procedure. To evaluate the performance, we submitted the outcome of the algorithm to the reverse engineering assessment competition DREAM2, where we used the data corresponding to the InSillico1 and InSilico2 networks as input. Our algorithm outperformed all other algorithms when inferring one of the directed gene-to-gene networks.
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| 46. |
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| 47. |
- Gustafsson, Mika, et al.
(författare)
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System Analysis of Gene Regulatory Networks
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- The inference of genome-wide regulatory networks in cells from high-throughput data sets has revealed a diverse picture of only partly overlapping descriptions. Nevertheless, several conclusions of the large-scale properties in the organization of these networks are possible. For example, the presence of hubs, a modular structure and certain motifs are recurrent phenomena. Several authors have recently claimed cell systems to be stable against perturbations and random errors, but still able to rapidly switch between different states from specific stimuli. Since inferred genome-wide systems need to be extremely simple to avoid overfitting, these two features are hard to attain simultaneously in a mathematical model. Here we review and discuss possible measures of how system stability and flexibility may be manifested and measured for linear ODE models. Furthermore, we review how different network properties contribute to these systems level properties. It turns out that the presence of repressed hubs, together with other phenomena of topological nature such as motifs and modules, contributes to the overall stability and/or flexibility of the system.
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| 48. |
- Hellberg, Sandra, et al.
(författare)
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Dynamic Response Genes in CD4+ T Cells Reveal a Network of Interactive Proteins that Classifies Disease Activity in Multiple Sclerosis
- 2016
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Ingår i: Cell Reports. - : Cell Press. - 2211-1247. ; 16:11, s. 2928-2939
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Tidskriftsartikel (refereegranskat)abstract
- Multiple sclerosis (MS) is a chronic inflammatory disease of the CNS and has a varying disease course as well as variable response to treatment. Biomarkers may therefore aid personalized treatment. We tested whether in vitro activation of MS patient-derived CD4+ T cells could reveal potential biomarkers. The dynamic gene expression response to activation was dysregulated in patient-derived CD4+ T cells. By integrating our findings with genome-wide association studies, we constructed a highly connected MS gene module, disclosing cell activation and chemotaxis as central components. Changes in several module genes were associated with differences in protein levels, which were measurable in cerebrospinal fluid and were used to classify patients from control individuals. In addition, these measurements could predict disease activity after 2 years and distinguish low and high responders to treatment in two additional, independent cohorts. While further validation is needed in larger cohorts prior to clinical implementation, we have uncovered a set of potentially promising biomarkers.
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| 49. |
- Hellberg, Sandra, et al.
(författare)
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Progesterone Dampens Immune Responses in In Vitro Activated CD4(+) T Cells and Affects Genes Associated With Autoimmune Diseases That Improve During Pregnancy
- 2021
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Ingår i: Frontiers in Immunology. - : Frontiers Media SA. - 1664-3224. ; 12
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Tidskriftsartikel (refereegranskat)abstract
- The changes in progesterone (P4) levels during and after pregnancy coincide with the temporary improvement and worsening of several autoimmune diseases like multiple sclerosis (MS) and rheumatoid arthritis (RA). Most likely immune-endocrine interactions play a major role in these pregnancy-induced effects. In this study, we used next generation sequencing to investigate the direct effects of P4 on CD4(+) T cell activation, key event in pregnancy and disease. We report profound dampening effects of P4 on T cell activation, altering the gene and protein expression profile and reversing many of the changes induced during the activation. The transcriptomic changes induced by P4 were significantly enriched for genes associated with diseases known to be modulated during pregnancy such as MS, RA and psoriasis. STAT1 and STAT3 were significantly downregulated by P4 and their downstream targets were significantly enriched among the disease-associated genes. Several of these genes included well-known and disease-relevant cytokines, such as IL-12 beta, CXCL10 and OSM, which were further validated also at the protein level using proximity extension assay. Our results extend the previous knowledge of P4 as an immune regulatory hormone and support its importance during pregnancy for regulating potentially detrimental immune responses towards the semi-allogenic fetus. Further, our results also point toward a potential role for P4 in the pregnancy-induced disease immunomodulation and highlight the need for further studies evaluating P4 as a future treatment option.
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| 50. |
- Herrgårdh, Tilda, et al.
(författare)
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Hybrid modelling for stroke care : Review and suggestions of new approaches for risk assessment and simulation of scenarios
- 2021
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Ingår i: NeuroImage. - : Elsevier Science Ltd. - 2213-1582. ; 31
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Forskningsöversikt (refereegranskat)abstract
- Stroke is an example of a complex and multi-factorial disease involving multiple organs, timescales, and disease mechanisms. To deal with this complexity, and to realize Precision Medicine of stroke, mathematical models are needed. Such approaches include: 1) machine learning, 2) bioinformatic network models, and 3) mechanistic models. Since these three approaches have complementary strengths and weaknesses, a hybrid modelling approach combining them would be the most beneficial. However, no concrete approach ready to be implemented for a specific disease has been presented to date. In this paper, we both review the strengths and weaknesses of the three approaches, and propose a roadmap for hybrid modelling in the case of stroke care. We focus on two main tasks needed for the clinical setting: a) For stroke risk calculation, we propose a new two-step approach, where non-linear mixed effects models and bioinformatic network models yield biomarkers which are used as input to a machine learning model and b) For simulation of care scenarios, we propose a new four-step approach, which revolves around iterations between simulations of the mechanistic models and imputations of non-modelled or non-measured variables. We illustrate and discuss the different approaches in the context of Precision Medicine for stroke.
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